Vol 30 no 4 2014 pages 571ndash573BIOINFORMATICS APPLICATIONS NOTE doi101093bioinformaticsbtt705

Gene expression Advance Access publication December 3 2013

Cascade a R package to study predict and simulate the diffusion

of a signal through a temporal gene networkNicolas Jung12 Frederic Bertrand2 Seiamak Bahram1 Laurent Vallat1 andMyriam Maumy-Bertrand2

1INSERM UMR S_1109 Labex Transplantex FMTS Hopitaux and Faculte de Medecine Universite de Strasbourg67085 Strasbourg Cedex and 2IRMA CNRS UMR 7501 Labex IRMIA Universite de Strasbourg 67084 StrasbourgCedex France

Associate Editor Ziv Bar-Joseph

ABSTRACT

Summary Temporal gene interactions in response to environmental

stress form a complex system that can be efficiently described using

gene regulatory networks They allow highlighting the more influential

genes and spotting some targets for biological intervention experi-

ments Despite that many reverse engineering tools have been

designed the Cascade package is an integrated solution adding

several new and original key features such as the ability to predict

changes in gene expressions after a biological perturbation in the

network and graphical outputs that allow monitoring the spread of a

signal through the network

Availability and implementation The R package Cascade is

available online at httpwww-mathu-strasbgfrgenpredspipphp

rubrique4

Contact fbertranmathunistrafr

Supplementary information Supplementary data are available at

Bioinformatics online

Received on June 6 2013 revised on October 4 2013 accepted on

November 28 2013

1 INTRODUCTION

Since the emergence of high-throughput technologies that allow

measuring simultaneously expression of thousands of genes

many tools have been developed to learn gene expression profiles

and reverse engineer their underlying gene regulatory network

(GRN) (Bar-Joseph et al 2012 Hecker et al 2009) These

tools are either based on static coexpression methods or if the

biological phenomenon shows any temporality time-dependent

methods Although the former relies on the assumption that coex-

pressed genes share some biological characteristics the latter

infers a directed network with temporal dependencies In this

last case another important distinction should be made between

exogenous stress (eg growth response) and endogenous phenom-

enon (eg cell cycle) (Yosef et al 2011 Zhu et al 2007) This

leads to different network topologies in exogenous stress net-

worksrsquo topologies seem to have larger hubs and shorter paths

through temporal-dependent transcriptional waves (Luscombe

et al 2004) This results in a quick response to environmental

modifications (Luscombe et al 2004) The Cascade package

is designed to model such lsquocascade networksrsquo taking advantageof the assignment of genes to temporal clusters which adds

temporal causality in the network

2 DETAILS ON THE PACKAGE FEATURES

This package has been designed to analyze temporal microarraydatasets allowing gene selection temporal cluster assignmentreverse engineering the GRN using a penalized regression

model and predicting the effect of biological intervention experi-ments It also features a temporal synthetic cascade simulation

tool The biological interpretations are facilitated thanks toseveral graphical outputs More insight about the statisticaltools as well as benchmarks is provided in Vallat et al (2013)

21 Gene selection and cluster assignment

Selecting the genes for reverse engineering is a crucial stepBesides selecting genes with high-differential expressions the

Cascade package allows enriching the selection with genesfeaturing specific temporal patterns As pointed out by Hao

and Baltimore (2009) several temporal gene expression wavescorresponding to specific cellular functions can be individualizedafter stimulation of the cellular environment In this pulsed bio-

logical response some relevant genes may have low but system-atic differential expressions This selection step mostly relies on

the Bioconductor R package limma (Smyth et al 2005)Each gene must be then assigned to one of the time clusters

This can be automatically performed (according to the first time

when the gene is differentially expressed) Alternatively the timeclusters can be user-provided

22 Reverse engineering of the network

The reverse engineering algorithm is the Lasso-penalized estima-tion of a linear regression model described in Vallat et al (2013)The Lasso penalty ensures sparsity which is a well-known fea-

ture of most biological networks (Barabasi 2003) Furthermorethe temporal gene clusters are taken into account using a set of

matrices F to describe how genes interact

Y frac14XN

ifrac141

FmethXiTHORNmethYTHORNiXi thorn eth1THORN

where Y is the regulated gene and the Xi are potential regulatorgenes the i determine the strength of the link between Xi and Y

methTHORN is the function thatmaps a gene to its temporal cluster and isTo whom correspondence should be addressed

The Author 2013 Published by Oxford University Press All rights reserved For Permissions please e-mail journalspermissionsoupcom 571

a noise Some further constraints are set to ensure a temporal

causality andweuse the Lasso estimator to achieve some sparsityIt is common knowledge that biological networks are scale-

free (Barabasi 2003) the distribution of the outgoing edges in

the networks follows a power law distribution As a consequence

using a statistical test from Clauset et al (2009) we derived a

cutoff value for the coefficients It was established by a simu-

lation study that such a procedure greatly improves F-scores

(Van Rijsbergen 1979) A graphical output Supplementary

Material S1 shows the modification of the network topology

when this cutoff varies For a given cutoff a graphical output

Supplementary Material S2 shows how the stimulated transcrip-

tional response spreads through the network If time clusters are

heterogeneous matrices F and values are iteratively estimated

in a coordinate ascendant approach On the contrary if all the

time clusters are homogeneous enough the estimation of the

matrices Fmay be achieved using all the genes in each of the clus-

ters instead of using only those pointed out by their values

This results in a non-iterative algorithm matrices F and values

are only estimated once

23 Prediction

We can predict changes in gene expressions using Equation (1)

after a gene intervention experiment at the first time point as

validated in silico and biologically in Vallat et al (2013)

24 Simulation

The Cascade package provides two simulation tools On the first

hand a random network can be simulated following the prefer-

ential attachment theory (Barabasi 2003) with some constraints

to ensure that the result is a temporal cascade network On the

other hand the model Equation (1) can be used to simulate

gene expressions from any given network

3 EXAMPLES

Two packagersquos vignettes detail the comprehensive analysis of two

example datasets A first dataset extracted from GSE39411 is

based on the transcriptional response of healthy lymphocytes

B-cells after antigenic stimulation (Vallat et al 2007) The

second dataset (E-MTAB-1475) has a different experimental

design and is based on the transcriptional response of murine

lymphocytes T-cells after an in vitro stimulation that sustains

cellular differentiation (van den Ham et al 2013) In both

cases gene expressions measured at different time points after

cell stimulation are used to select genes with specific temporal

patterns or high differential expressions which are then assigned

to time clusters (Fig 1 for GSE39411 and Supplementary

Material S3 and S4 for E-MTAB-1475) The reverse engineering

of the GRN highlights the most influential genes in the temporal

cascade (Fig 2 and Supplementary Material S3ndashS5) The impact

in the GRN of a knockdown experiment of one influential gene

is predicted (Fig 3 and Supplementary Material S3ndashS6)

ACKNOWLEDGEMENT

Nicolas Jung Frederic Bertrand Laurent Vallat and Myriam

Maumy-Bertrand have contributed equally to this article

Funding Strasbourg High Throughput Next Generation

Sequencing facility (GENOMAX) INSERM UMR_S 1109

CNRS UMR 7501 University of Strasbourg INSERM

Fig 3 Step 3 predicted perturbations in the network at the second time

point after gene expression modulation at an early time in the temporal

GRN of GSE39411 The green influential gene is supposed to be knocked

down Color scale legend from downregulated (blue) to upregulated (red)

genes

Fig 2 Step 2 Reverse engineering of the network in GSE39411 Nodes

represent genes and the arrows statistical links between the genes

Arrowsrsquo thickness depicts the intensity of the link

Fig 1 Step 1 gene selection in GSE39411 and assignment to a time

cluster

572

NJung et al

(ITMO cancer Systems Biology plan cancer 2009-2013) CNRS(PEPS-BMI interdisciplinarity 2013)

Conflict of Interest none declared

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BarabasiAL (2003) Emergence of scaling in complex networks In BornholdtS

and SchusterHG (eds) Handbook of Graphs and Networks From the Genome

to the Internet Wiley-VCH Weinheim pp 69ndash84

Bar-JosephZ et al (2012) Studying and modelling dynamic biological processes

using time-series gene expression data Nat Rev Genet 13 552ndash564

ClausetA et al (2009) Power-law distributions in empirical data SIAM Rev 51

661ndash703

HaoS and BaltimoreD (2009) The stability of mRNA influences the temporal

order of the induction of genes encoding inflammatory molecules Nat

Immunol 10 281ndash288

HeckerM et al (2009) Gene regulatory network inference data integration in

dynamic models-a review Biosystems 96 86ndash103

LuscombeNM et al (2004) Genomic analysis of regulatory network dynamics

reveals large topological changes Nature 431 308ndash312

SmythGK (2005) Limma linear models for microarray data In GentlemanR

et al (eds) Bioinformatics and Computational Biology Solutions Using R and

Bioconductor Springer New York pp 397ndash420

van den HamHJ et al (2013) Early divergence of Th1 and Th2 transcriptomes

involves a small core response and sets of transiently expressed genes Eur J

Immunol 43 1074ndash1084

Van RijsbergenCJ (1979) Information Retrieval 2nd edn Butterworth London

VallatL et al (2007) Temporal genetic program following B-cell receptor cross-

linking altered balance between proliferation and death in healthy and malig-

nant B cells Blood 109 3989ndash3997

VallatL et al (2013) Reverse-engineering the genetic circuitry of a cancer cell with

predicted intervention in chronic lymphocytic leukemia Proc Natl Acad Sci

USA 110 459ndash464

YosefN and RegevA (2011) Impulse control temporal dynamics in gene

transcription Cell 144 886ndash896

ZhuX et al (2007) Getting connected analysis and principles of biological

networks Genes Dev 21 1010ndash1024

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Cascade